{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "initial_id",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-23T13:42:31.356120Z",
     "start_time": "2025-01-23T13:42:31.348266Z"
    },
    "execution": {
     "iopub.execute_input": "2025-01-23T13:58:04.731268Z",
     "iopub.status.busy": "2025-01-23T13:58:04.731111Z",
     "iopub.status.idle": "2025-01-23T13:58:06.864889Z",
     "shell.execute_reply": "2025-01-23T13:58:06.864393Z",
     "shell.execute_reply.started": "2025-01-23T13:58:04.731248Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sys.version_info(major=3, minor=10, micro=14, releaselevel='final', serial=0)\n",
      "matplotlib 3.10.0\n",
      "numpy 1.26.4\n",
      "pandas 2.2.3\n",
      "sklearn 1.6.0\n",
      "torch 2.5.1+cu124\n",
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import pandas as pd\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "from tqdm.auto import tqdm\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "print(sys.version_info)\n",
    "for module in mpl, np, pd, sklearn, torch:\n",
    "    print(module.__name__, module.__version__)\n",
    "\n",
    "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "print(device)\n",
    "\n",
    "seed = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1fa33d42-9c09-4695-ab8d-d7720f52dd98",
   "metadata": {
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-01-23T13:58:06.866465Z",
     "iopub.status.busy": "2025-01-23T13:58:06.866064Z",
     "iopub.status.idle": "2025-01-23T13:58:06.868923Z",
     "shell.execute_reply": "2025-01-23T13:58:06.868385Z",
     "shell.execute_reply.started": "2025-01-23T13:58:06.866443Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# !pip install tensorflow"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d5488202f3a7bd8",
   "metadata": {},
   "source": [
    "## 准备数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "99e15a8bf4ac2398",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-23T13:42:34.985109Z",
     "start_time": "2025-01-23T13:42:31.510155Z"
    },
    "execution": {
     "iopub.execute_input": "2025-01-23T13:58:06.869695Z",
     "iopub.status.busy": "2025-01-23T13:58:06.869511Z",
     "iopub.status.idle": "2025-01-23T14:03:44.793344Z",
     "shell.execute_reply": "2025-01-23T14:03:44.792727Z",
     "shell.execute_reply.started": "2025-01-23T13:58:06.869675Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-01-23 21:58:07.022518: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2025-01-23 21:58:07.034438: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "E0000 00:00:1737640687.048991     526 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "E0000 00:00:1737640687.053421     526 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2025-01-23 21:58:07.068358: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz\n",
      "\u001b[1m17464789/17464789\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m332s\u001b[0m 19us/step\n"
     ]
    }
   ],
   "source": [
    "from tensorflow import keras\n",
    "\n",
    "# 用karas有的数据集imdb，电影分类,分电影是积极的，还是消极的\n",
    "imdb = keras.datasets.imdb\n",
    "\n",
    "# 载入数据使用下面两个参数\n",
    "# 词典大小，仅保留训练数据中前10000个最经常出现的单词，低频单词被舍弃\n",
    "# 前一万个词出现词频最高的会保留下来进行处理，后面的作为特殊字符处理，\n",
    "vocab_size = 10000\n",
    "index_from = 3  # 0,1,2,3空出来做别的事\n",
    "# 小于3的id都是特殊字符\n",
    "# 需要注意的一点是取出来的词表还是从1开始的，需要做处理\n",
    "\n",
    "# 下载数据集，并将数据集分为训练集和测试集\n",
    "(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=vocab_size, index_from=index_from)\n",
    "# train_data和test_data的类型都是列表的列表\n",
    "# 每个列表代表对应文本中每个单词的id值所组成的列表，只记录出现频率最高的前10000个单词\n",
    "# train_labels和test_labels的类型都是numpy.ndarray类型列表,是二分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ba986276e2fb2d8c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-23T13:42:35.043565Z",
     "start_time": "2025-01-23T13:42:34.986110Z"
    },
    "execution": {
     "iopub.execute_input": "2025-01-23T14:03:44.794904Z",
     "iopub.status.busy": "2025-01-23T14:03:44.794457Z",
     "iopub.status.idle": "2025-01-23T14:03:46.544467Z",
     "shell.execute_reply": "2025-01-23T14:03:46.543916Z",
     "shell.execute_reply.started": "2025-01-23T14:03:44.794882Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb_word_index.json\n",
      "\u001b[1m1641221/1641221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1us/step\n",
      "88584\n",
      "<class 'dict'>\n"
     ]
    }
   ],
   "source": [
    "# 载入词表，看下词表长度，词表就像英语字典\n",
    "word_index = imdb.get_word_index()\n",
    "print(len(word_index))\n",
    "print(type(word_index))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efa6a9ce868c9b65",
   "metadata": {},
   "source": [
    "## 构造 word2idx 和 idx2word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "270bc30c8df50599",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-23T13:42:35.075845Z",
     "start_time": "2025-01-23T13:42:35.044568Z"
    },
    "execution": {
     "iopub.execute_input": "2025-01-23T14:03:46.545377Z",
     "iopub.status.busy": "2025-01-23T14:03:46.545105Z",
     "iopub.status.idle": "2025-01-23T14:03:46.575534Z",
     "shell.execute_reply": "2025-01-23T14:03:46.574944Z",
     "shell.execute_reply.started": "2025-01-23T14:03:46.545357Z"
    }
   },
   "outputs": [],
   "source": [
    "# 0,1,2,3空出来做别的事,这里的idx是从1开始的,所以加3\n",
    "word2idx = {word: idx + 3 for word, idx in word_index.items()}\n",
    "\n",
    "word2idx.update({\n",
    "    \"[PAD]\": 0,  # 填充 token\n",
    "    \"[BOS]\": 1,  # begin of sentence，句子开始\n",
    "    \"[UNK]\": 2,  # 未知 token\n",
    "    \"[EOS]\": 3  # end of sentence，句子结束\n",
    "})\n",
    "\n",
    "# # 反向词典,id变为单词\n",
    "idx2word = {idx: word for word, idx in word2idx.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3425a418cd0677a5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-23T13:42:35.926855Z",
     "start_time": "2025-01-23T13:42:35.077853Z"
    },
    "execution": {
     "iopub.execute_input": "2025-01-23T14:03:46.576703Z",
     "iopub.status.busy": "2025-01-23T14:03:46.576263Z",
     "iopub.status.idle": "2025-01-23T14:03:47.554816Z",
     "shell.execute_reply": "2025-01-23T14:03:47.554187Z",
     "shell.execute_reply.started": "2025-01-23T14:03:46.576682Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 获取 max_length\n",
    "length_collect = {}\n",
    "\n",
    "#统计样本中每个长度出现的次数\n",
    "for text in train_data:\n",
    "    length = len(text)  # 句子长度\n",
    "    # 如果 length 已经存在于 length_collect 字典中，则将其对应的值加 1。\n",
    "    # 如果 length 不存在于 length_collect 字典中，则将其添加到字典中，并将其值初始化为 1。\n",
    "    length_collect[length] = length_collect.get(length, 0) + 1  # 统计每个长度出现的次数\n",
    "\n",
    "MAX_LENGTH = 500\n",
    "plt.bar(length_collect.keys(), length_collect.values())  # 画出每个长度出现的次数的柱状图\n",
    "# axvline():用于在图表中绘制一条垂直于 x 轴的直线。第一个参数是指定直线的位置。\n",
    "plt.axvline(MAX_LENGTH, label=\"max length\", c=\"gray\", ls=\":\")\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "83e8b75c0824ebbe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-23T13:42:36.089763Z",
     "start_time": "2025-01-23T13:42:35.927866Z"
    },
    "execution": {
     "iopub.execute_input": "2025-01-23T14:03:47.556999Z",
     "iopub.status.busy": "2025-01-23T14:03:47.556738Z",
     "iopub.status.idle": "2025-01-23T14:03:47.742751Z",
     "shell.execute_reply": "2025-01-23T14:03:47.742256Z",
     "shell.execute_reply.started": "2025-01-23T14:03:47.556979Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 相对句子长度画个直方图，看看长度分布\n",
    "length_list = [len(text) for text in train_data]\n",
    "plt.hist(length_list, bins=50)\n",
    "plt.xlabel(\"length\")\n",
    "plt.ylabel(\"frequency\")\n",
    "plt.axvline(500, label=\"max length\", c=\"gray\", ls=\":\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b9c3c5072d49c29",
   "metadata": {},
   "source": [
    "## Tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b58b84ec603ebc7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-23T13:42:36.105738Z",
     "start_time": "2025-01-23T13:42:36.090780Z"
    },
    "execution": {
     "iopub.execute_input": "2025-01-23T14:03:47.743540Z",
     "iopub.status.busy": "2025-01-23T14:03:47.743353Z",
     "iopub.status.idle": "2025-01-23T14:03:47.751409Z",
     "shell.execute_reply": "2025-01-23T14:03:47.750905Z",
     "shell.execute_reply.started": "2025-01-23T14:03:47.743521Z"
    }
   },
   "outputs": [],
   "source": [
    "class Tokenizer:\n",
    "    def __init__(self, word2idx, idx2word, max_length=500,\n",
    "                 pad_idx=0, bos_idx=1, eos_idx=3, unk_idx=2):\n",
    "        self.word2idx = word2idx  # 词典，单词->id\n",
    "        self.idx2word = idx2word  # 反向词典，id->单词\n",
    "        self.max_length = max_length\n",
    "        self.pad_idx = pad_idx  #填充\n",
    "        self.bos_idx = bos_idx  #开始\n",
    "        self.eos_idx = eos_idx  #结束\n",
    "        self.unk_idx = unk_idx  #未知，未出现在最高频词表中的词\n",
    "\n",
    "    def encode(self, test_list, padding_first=False):\n",
    "        \"\"\"\n",
    "        将文本列表转化为索引列表\n",
    "        :param test_list: 当前批次的文本列表\n",
    "        \"\"\"\n",
    "        # 最大长度，最大长度是500，\n",
    "        # 但是如果句子长度小于500，就取句子长度（句子长度是本组句子中最长的），\n",
    "        # 2是为了留出开始和结束的位置\n",
    "        max_length = min(self.max_length, 2 + max([len(text) for text in test_list]))\n",
    "        indices_list = []\n",
    "        for text in test_list:\n",
    "            # 直接切片取前max_length-2个单词，然后加上bos和eos\n",
    "            indices = [self.bos_idx] + [self.word2idx.get(word, self.unk_idx) for word in text[:max_length - 2]] + [self.eos_idx]\n",
    "            if padding_first:  # 填充在最前面\n",
    "                indices = [self.pad_idx] * (max_length - len(indices)) + indices\n",
    "            else:  # 填充在最后面\n",
    "                indices = indices + [self.pad_idx] * (max_length - len(indices))\n",
    "            indices_list.append(indices)\n",
    "        return torch.tensor(indices_list)  # 二维列表转化为tensor\n",
    "\n",
    "    def decode(self, indices_list, remove_bos=True,\n",
    "               remove_eos=True, remove_pad=True, split=False):\n",
    "        \"\"\"\n",
    "        将索引列表转化为文本列表\n",
    "        \"\"\"\n",
    "        text_list = []\n",
    "        for indices in indices_list:\n",
    "            text = []\n",
    "            for index in indices:\n",
    "                word = self.idx2word.get(index, \"[UNK]\")\n",
    "                if remove_bos and word == \"[BOS]\": continue\n",
    "                if remove_eos and word == \"[EOS]\": break\n",
    "                if remove_pad and word == \"[PAD]\": break\n",
    "                text.append(word)\n",
    "            text_list.append(\" \".join(text) if not split else text)\n",
    "        return text_list\n",
    "\n",
    "\n",
    "tokenizer = Tokenizer(word2idx, idx2word, max_length=MAX_LENGTH)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c5986d8827d9b25",
   "metadata": {},
   "source": [
    "## 数据集与DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "73712533465907cc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-23T13:42:38.340296Z",
     "start_time": "2025-01-23T13:42:36.106743Z"
    },
    "execution": {
     "iopub.execute_input": "2025-01-23T14:03:47.752388Z",
     "iopub.status.busy": "2025-01-23T14:03:47.751944Z",
     "iopub.status.idle": "2025-01-23T14:03:51.108259Z",
     "shell.execute_reply": "2025-01-23T14:03:51.107730Z",
     "shell.execute_reply.started": "2025-01-23T14:03:47.752366Z"
    }
   },
   "outputs": [],
   "source": [
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "\n",
    "class IMDBDataset(Dataset):\n",
    "    def __init__(self, data, labels, remain_length=True):\n",
    "        if remain_length:  # 打印【BOS】，【EOS】，【PAD】\n",
    "            self.data = tokenizer.decode(data, remove_bos=False,\n",
    "                                         remove_eos=False, remove_pad=False)\n",
    "        else:  # 不打印【BOS】，【EOS】，【PAD】\n",
    "            self.data = tokenizer.decode(data)\n",
    "        self.labels = labels\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        text = self.data[index]\n",
    "        label = self.labels[index]\n",
    "        return text, label\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data)\n",
    "\n",
    "\n",
    "def collate_fct(batch):\n",
    "    \"\"\"\n",
    "    将batch数据处理成tensor形式\n",
    "    \"\"\"\n",
    "    # batch是128样本，每个样本类型是元组，第一个元素是文本，第二个元素是标签\n",
    "    text_list = [item[0].split() for item in batch]\n",
    "    label_list = [item[1] for item in batch]\n",
    "    # 文本转化为索引\n",
    "    text_list = tokenizer.encode(text_list,padding_first=True).to(dtype=torch.int)\n",
    "    # torch.tensor(label_list):将 Python 列表 label_list 转换为 PyTorch 张量。\n",
    "    # .reshape(-1, 1):将张量的形状重塑为 (n, 1)，其中 n 是 label_list 的长度。\n",
    "    # .to(dtype=torch.float):将张量的数据类型转换为 float。\n",
    "    return text_list, torch.tensor(label_list).reshape(-1, 1).to(dtype=torch.float)\n",
    "\n",
    "# 用RNN，缩短序列长度\n",
    "train_ds = IMDBDataset(train_data, train_labels,remain_length=False)\n",
    "test_ds = IMDBDataset(test_data, test_labels,remain_length=False)\n",
    "\n",
    "batch_size = 128\n",
    "# collate_fn是处理batch的函数\n",
    "train_loader = DataLoader(train_ds, batch_size=batch_size,\n",
    "                          shuffle=True, collate_fn=collate_fct)\n",
    "test_loader = DataLoader(test_ds, batch_size=batch_size,\n",
    "                         shuffle=False, collate_fn=collate_fct)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7001311e460c0b99",
   "metadata": {},
   "source": [
    "## 定义模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "22ab9152ea499fd4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-23T13:42:38.346078Z",
     "start_time": "2025-01-23T13:42:38.341299Z"
    },
    "execution": {
     "iopub.execute_input": "2025-01-23T14:03:51.109290Z",
     "iopub.status.busy": "2025-01-23T14:03:51.108870Z",
     "iopub.status.idle": "2025-01-23T14:03:51.114439Z",
     "shell.execute_reply": "2025-01-23T14:03:51.113957Z",
     "shell.execute_reply.started": "2025-01-23T14:03:51.109270Z"
    }
   },
   "outputs": [],
   "source": [
    "class RNN(nn.Module):\n",
    "    def __init__(self,embedding_dim=16, hidden_dim=64, vocab_size=vocab_size, num_layers=1,bidirectional=False):\n",
    "        super(RNN, self).__init__()\n",
    "        # 词嵌入层\n",
    "        self.embedding = nn.Embedding(vocab_size, embedding_dim)\n",
    "        # 循环神经网络层\n",
    "        # num_layers: 隐藏层的层数\n",
    "        # batch_first: 输入输出的格式，默认是(seq,batch,feature)，设置为True后为(batch,seq,feature)\n",
    "        # bidirectional: 是否使用双向循环神经网络\n",
    "        self.rnn=nn.RNN(embedding_dim,hidden_dim,num_layers=num_layers,batch_first=True,bidirectional=bidirectional)\n",
    "        # 全连接层\n",
    "        self.layer=nn.Linear(hidden_dim*(2 if bidirectional else 1),hidden_dim)\n",
    "        # 输出层\n",
    "        self.fc=nn.Linear(hidden_dim,1)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        # [bacth_size,seq_len,vocab_size]->[batch_size,seq_len,embedding_dim]\n",
    "        # [batch_size, 500,10000]->[batch_size, 500,16]\n",
    "        x=self.embedding(x) # 词嵌入\n",
    "        # [batch_size,seq_len,embedding_dim]->[batch_size,seq_len,hidden_dim]\n",
    "        #                                或 [batch_size,seq_len,hidden_dim*2] \n",
    "        seq_output,final_hidden=self.rnn(x) # 循环神经网络\n",
    "        # [batch_size,seq_len,hidden_dim]->[batch_size,1,hidden_dim]\n",
    "        # 取最后一个时间步的输出 (这也是为什么要设置padding_first=True的原因)\n",
    "        x=seq_output[:,-1,:] # 取最后一个时序输出作为全连接层的输入\n",
    "        # [batch_size,1,hidden_dim]->[batch_size,hidden_dim]\n",
    "        x=self.layer(x) # 全连接层\n",
    "        # [batch_size,hidden_dim]->[batch_size,1]\n",
    "        x=self.fc(x) # 输出层\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "806fc209f7870230",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-23T13:42:38.353666Z",
     "start_time": "2025-01-23T13:42:38.347081Z"
    },
    "execution": {
     "iopub.execute_input": "2025-01-23T14:03:51.115208Z",
     "iopub.status.busy": "2025-01-23T14:03:51.114939Z",
     "iopub.status.idle": "2025-01-23T14:03:51.139760Z",
     "shell.execute_reply": "2025-01-23T14:03:51.139316Z",
     "shell.execute_reply.started": "2025-01-23T14:03:51.115189Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "单层单向\n",
      "            embedding.weight            paramerters num: 160000\n",
      "            rnn.weight_ih_l0            paramerters num: 1024\n",
      "            rnn.weight_hh_l0            paramerters num: 4096\n",
      "             rnn.bias_ih_l0             paramerters num: 64\n",
      "             rnn.bias_hh_l0             paramerters num: 64\n",
      "              layer.weight              paramerters num: 4096\n",
      "               layer.bias               paramerters num: 64\n",
      "               fc.weight                paramerters num: 64\n",
      "                fc.bias                 paramerters num: 1\n"
     ]
    }
   ],
   "source": [
    "print(\"单层单向\")\n",
    "for key, value in RNN().named_parameters():\n",
    "    print(f\"{key:^40}paramerters num: {np.prod(value.shape)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fed2911ff9c09230",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-23T13:42:38.361890Z",
     "start_time": "2025-01-23T13:42:38.355672Z"
    },
    "execution": {
     "iopub.execute_input": "2025-01-23T14:03:51.140434Z",
     "iopub.status.busy": "2025-01-23T14:03:51.140258Z",
     "iopub.status.idle": "2025-01-23T14:03:51.145383Z",
     "shell.execute_reply": "2025-01-23T14:03:51.144893Z",
     "shell.execute_reply.started": "2025-01-23T14:03:51.140416Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "单层双向\n",
      "            embedding.weight            paramerters num: 160000\n",
      "            rnn.weight_ih_l0            paramerters num: 1024\n",
      "            rnn.weight_hh_l0            paramerters num: 4096\n",
      "             rnn.bias_ih_l0             paramerters num: 64\n",
      "             rnn.bias_hh_l0             paramerters num: 64\n",
      "        rnn.weight_ih_l0_reverse        paramerters num: 1024\n",
      "        rnn.weight_hh_l0_reverse        paramerters num: 4096\n",
      "         rnn.bias_ih_l0_reverse         paramerters num: 64\n",
      "         rnn.bias_hh_l0_reverse         paramerters num: 64\n",
      "              layer.weight              paramerters num: 8192\n",
      "               layer.bias               paramerters num: 64\n",
      "               fc.weight                paramerters num: 64\n",
      "                fc.bias                 paramerters num: 1\n"
     ]
    }
   ],
   "source": [
    "print(\"单层双向\")\n",
    "for key, value in RNN(bidirectional=True).named_parameters():\n",
    "    print(f\"{key:^40}paramerters num: {np.prod(value.shape)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "adb61c1091d9fcd8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-23T13:42:38.368653Z",
     "start_time": "2025-01-23T13:42:38.362893Z"
    },
    "execution": {
     "iopub.execute_input": "2025-01-23T14:03:51.146088Z",
     "iopub.status.busy": "2025-01-23T14:03:51.145905Z",
     "iopub.status.idle": "2025-01-23T14:03:51.150974Z",
     "shell.execute_reply": "2025-01-23T14:03:51.150528Z",
     "shell.execute_reply.started": "2025-01-23T14:03:51.146069Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "双层单向\n",
      "            embedding.weight            paramerters num: 160000\n",
      "            rnn.weight_ih_l0            paramerters num: 1024\n",
      "            rnn.weight_hh_l0            paramerters num: 4096\n",
      "             rnn.bias_ih_l0             paramerters num: 64\n",
      "             rnn.bias_hh_l0             paramerters num: 64\n",
      "            rnn.weight_ih_l1            paramerters num: 4096\n",
      "            rnn.weight_hh_l1            paramerters num: 4096\n",
      "             rnn.bias_ih_l1             paramerters num: 64\n",
      "             rnn.bias_hh_l1             paramerters num: 64\n",
      "              layer.weight              paramerters num: 4096\n",
      "               layer.bias               paramerters num: 64\n",
      "               fc.weight                paramerters num: 64\n",
      "                fc.bias                 paramerters num: 1\n"
     ]
    }
   ],
   "source": [
    "print(\"双层单向\")\n",
    "for key, value in RNN(num_layers=2).named_parameters():\n",
    "    print(f\"{key:^40}paramerters num: {np.prod(value.shape)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1e33e38523cade22",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-23T13:42:38.375718Z",
     "start_time": "2025-01-23T13:42:38.369656Z"
    },
    "execution": {
     "iopub.execute_input": "2025-01-23T14:03:51.151818Z",
     "iopub.status.busy": "2025-01-23T14:03:51.151527Z",
     "iopub.status.idle": "2025-01-23T14:03:51.156882Z",
     "shell.execute_reply": "2025-01-23T14:03:51.156429Z",
     "shell.execute_reply.started": "2025-01-23T14:03:51.151798Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "双层双向\n",
      "            embedding.weight            paramerters num: 160000\n",
      "            rnn.weight_ih_l0            paramerters num: 1024\n",
      "            rnn.weight_hh_l0            paramerters num: 4096\n",
      "             rnn.bias_ih_l0             paramerters num: 64\n",
      "             rnn.bias_hh_l0             paramerters num: 64\n",
      "        rnn.weight_ih_l0_reverse        paramerters num: 1024\n",
      "        rnn.weight_hh_l0_reverse        paramerters num: 4096\n",
      "         rnn.bias_ih_l0_reverse         paramerters num: 64\n",
      "         rnn.bias_hh_l0_reverse         paramerters num: 64\n",
      "            rnn.weight_ih_l1            paramerters num: 8192\n",
      "            rnn.weight_hh_l1            paramerters num: 4096\n",
      "             rnn.bias_ih_l1             paramerters num: 64\n",
      "             rnn.bias_hh_l1             paramerters num: 64\n",
      "        rnn.weight_ih_l1_reverse        paramerters num: 8192\n",
      "        rnn.weight_hh_l1_reverse        paramerters num: 4096\n",
      "         rnn.bias_ih_l1_reverse         paramerters num: 64\n",
      "         rnn.bias_hh_l1_reverse         paramerters num: 64\n",
      "              layer.weight              paramerters num: 8192\n",
      "               layer.bias               paramerters num: 64\n",
      "               fc.weight                paramerters num: 64\n",
      "                fc.bias                 paramerters num: 1\n"
     ]
    }
   ],
   "source": [
    "print(\"双层双向\")\n",
    "for key, value in RNN(num_layers=2,bidirectional=True).named_parameters():\n",
    "    print(f\"{key:^40}paramerters num: {np.prod(value.shape)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ef16b2489a34e67",
   "metadata": {},
   "source": [
    "## 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ff68a430988cfb82",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-23T13:42:38.451924Z",
     "start_time": "2025-01-23T13:42:38.376721Z"
    },
    "execution": {
     "iopub.execute_input": "2025-01-23T14:03:51.157720Z",
     "iopub.status.busy": "2025-01-23T14:03:51.157444Z",
     "iopub.status.idle": "2025-01-23T14:03:51.257566Z",
     "shell.execute_reply": "2025-01-23T14:03:51.257087Z",
     "shell.execute_reply.started": "2025-01-23T14:03:51.157701Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "\n",
    "@torch.no_grad()  # 装饰器，禁止梯度计算\n",
    "def evaluate(model, data_loader, loss_fct):\n",
    "    loss_list = []\n",
    "    pred_list = []\n",
    "    label_list = []\n",
    "    for datas, labels in data_loader:\n",
    "        datas = datas.to(device)\n",
    "        labels = labels.to(device)\n",
    "\n",
    "        # 前向传播\n",
    "        logits = model(datas)\n",
    "        loss = loss_fct(logits, labels)  # 验证集损失\n",
    "        # tensor.item() 获取tensor的数值，loss是只有一个元素的tensor\n",
    "        loss_list.append(loss.item())\n",
    "\n",
    "        # 二分类\n",
    "        preds = logits > 0  # 预测值大于0的为1，否则为0\n",
    "        pred_list.extend(preds.cpu().numpy().tolist())\n",
    "        label_list.extend(labels.cpu().numpy().tolist())\n",
    "\n",
    "    acc = accuracy_score(label_list, pred_list)  # 计算准确率\n",
    "    return np.mean(loss_list), acc  # # 返回验证集平均损失和准确率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8c4b85e148a60f34",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-23T13:42:38.458954Z",
     "start_time": "2025-01-23T13:42:38.452927Z"
    },
    "execution": {
     "iopub.execute_input": "2025-01-23T14:03:51.258618Z",
     "iopub.status.busy": "2025-01-23T14:03:51.258142Z",
     "iopub.status.idle": "2025-01-23T14:03:51.263646Z",
     "shell.execute_reply": "2025-01-23T14:03:51.263154Z",
     "shell.execute_reply.started": "2025-01-23T14:03:51.258597Z"
    }
   },
   "outputs": [],
   "source": [
    "class SaveCheckpointsCallback:\n",
    "    def __init__(self, save_dir, save_step=500, save_best_only=True):\n",
    "        self.save_dir = save_dir  # 保存路径\n",
    "        self.save_step = save_step  # 保存步数\n",
    "        self.save_best_only = save_best_only  # 是否只保存最好的模型\n",
    "        self.best_metric = -1  # 最好的指标，指标不可能为负数，所以初始化为-1\n",
    "        # 创建保存路径\n",
    "        if not os.path.exists(self.save_dir):  # 如果不存在保存路径，则创建\n",
    "            os.makedirs(self.save_dir)\n",
    "\n",
    "    # 对象被调用时：当你将对象像函数一样调用时，Python 会自动调用 __call__ 方法。\n",
    "    # state_dict() 返回模型参数的字典，包括模型参数和优化器参数\n",
    "    # metric 是指标，可以是验证集的准确率，也可以是其他指标\n",
    "    def __call__(self, step, state_dict, metric=None):\n",
    "        if step % self.save_step > 0:\n",
    "            return  # 不是保存步数，则直接返回\n",
    "\n",
    "        if self.save_best_only:\n",
    "            assert metric is not None  # 必须传入metric\n",
    "            if metric >= self.best_metric:  # 如果当前指标大于最好的指标\n",
    "                # save checkpoint\n",
    "                # 保存最好的模型，覆盖之前的模型，不保存step，只保存state_dict，即模型参数，不保存优化器参数\n",
    "                torch.save(state_dict, os.path.join(self.save_dir, \"02_embedding_rnn.ckpt\"))\n",
    "                self.best_metric = metric  # 更新最好的指标\n",
    "        else:\n",
    "            # 保存模型\n",
    "            torch.save(state_dict, os.path.join(self.save_dir, f\"{step}.ckpt\"))\n",
    "            # 保存每个step的模型，不覆盖之前的模型，保存step，保存state_dict，即模型参数，不保存优化器参数\n"
   ]
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   "source": [
    "class EarlyStopCallback:\n",
    "    def __init__(self, patience=5, min_delta=0.01):\n",
    "        self.patience = patience  # 多少个step没有提升就停止训练\n",
    "        self.min_delta = min_delta  # 最小的提升幅度\n",
    "        self.best_metric = -1  # 记录的最好的指标\n",
    "        self.counter = 0  # 计数器，记录连续多少个step没有提升\n",
    "\n",
    "    def __call__(self, metric):\n",
    "        if metric >= self.best_metric + self.min_delta:  # 如果指标提升了\n",
    "            self.best_metric = metric  # 更新最好的指标\n",
    "            self.counter = 0  # 计数器清零\n",
    "        else:\n",
    "            self.counter += 1  # 计数器加一\n",
    "\n",
    "    @property  # 使用@property装饰器，使得 对象.early_stop可以调用，不需要()\n",
    "    def early_stop(self):\n",
    "        # 如果计数器大于等于patience，则返回True，停止训练\n",
    "        return self.counter >= self.patience"
   ]
  },
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   "id": "9d1b64287652db63",
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   "source": [
    "def training(model,\n",
    "             train_loader,\n",
    "             val_loader,\n",
    "             epoch,\n",
    "             loss_fct,\n",
    "             optimizer,\n",
    "             save_ckpt_callback=None,\n",
    "             early_stop_callback=None,\n",
    "             eval_step=500,\n",
    "             ):\n",
    "    record_dict = {\n",
    "        \"train\": [],\n",
    "        \"val\": []\n",
    "    }\n",
    "\n",
    "    global_step = 0  # 全局步数\n",
    "    model.train()  # 训练模式\n",
    "    with tqdm(total=epoch * len(train_loader)) as pbar:\n",
    "        for epoch_id in range(epoch):\n",
    "            for datas, labels in train_loader:\n",
    "                datas = datas.to(device)\n",
    "                labels = labels.to(device)\n",
    "\n",
    "                # 前向传播\n",
    "                logits = model(datas)\n",
    "                loss = loss_fct(logits, labels)  # 训练集损失\n",
    "                #当sigmoid输出大于0.5时，预测为1，否则预测为0，这里大于0，刚好sigmoid的值是0.5，预测为1\n",
    "                preds = logits > 0\n",
    "\n",
    "                # 反向传播\n",
    "                optimizer.zero_grad()  # 梯度清零\n",
    "                loss.backward()  # 反向传播\n",
    "                optimizer.step()  # 优化器更新参数\n",
    "\n",
    "                # 计算准确率\n",
    "                acc = accuracy_score(labels.cpu().numpy(), preds.cpu().numpy())\n",
    "                loss = loss.cpu().item()\n",
    "\n",
    "                record_dict[\"train\"].append({\n",
    "                    \"loss\": loss,\n",
    "                    \"acc\": acc,\n",
    "                    \"step\": global_step\n",
    "                })\n",
    "\n",
    "                # 评估\n",
    "                if global_step % eval_step == 0:\n",
    "                    model.eval()  # 评估模式\n",
    "                    # 验证集损失和准确率\n",
    "                    val_loss, val_acc = evaluate(model, val_loader, loss_fct)\n",
    "                    record_dict[\"val\"].append({\n",
    "                        \"loss\": val_loss,\n",
    "                        \"acc\": val_acc,\n",
    "                        \"step\": global_step\n",
    "                    })\n",
    "                    model.train()  # 训练模式\n",
    "\n",
    "                    # 2. 保存模型权重 save model checkpoint\n",
    "                    if save_ckpt_callback is not None:\n",
    "                        # model.state_dict() 返回模型参数的字典，包括模型参数和优化器参数\n",
    "                        save_ckpt_callback(global_step, model.state_dict(), val_acc)\n",
    "                        # 保存最好的模型，覆盖之前的模型，保存step，保存state_dict,通过metric判断是否保存最好的模型\n",
    "\n",
    "                    # 3. 早停 early stopping\n",
    "                    if early_stop_callback is not None:\n",
    "                        # 验证集准确率不再提升，则停止训练\n",
    "                        early_stop_callback(val_acc)\n",
    "                        # 验证集准确率不再提升，则停止训练\n",
    "                        if early_stop_callback.early_stop:\n",
    "                            print(f\"Early stop at epoch {epoch_id} / global_step {global_step}\")\n",
    "                            return record_dict  # 早停，返回记录字典 record_dict\n",
    "\n",
    "                # 更新进度条和全局步数\n",
    "                pbar.update(1)  # 更新进度条\n",
    "                global_step += 1  # 全局步数加一\n",
    "                pbar.set_postfix({\"epoch\": epoch_id})\n",
    "\n",
    "    return record_dict  # 训练结束，返回记录字典 record_dict\n"
   ]
  },
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   "execution_count": 19,
   "id": "1764120147b79f6",
   "metadata": {
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   "source": [
    "def plot_record_curves(record_dict, sample_step=500):\n",
    "    # .set_index(\"step\") 将 step 列设置为 DataFrame 的索引\n",
    "    train_df = pd.DataFrame(record_dict[\"train\"]).set_index(\"step\").iloc[::sample_step]\n",
    "    val_df = pd.DataFrame(record_dict[\"val\"]).set_index(\"step\")\n",
    "\n",
    "    last_step = train_df.index[-1]  # 最后一步的步数\n",
    "\n",
    "    # print(train_df)\n",
    "    # print(val_df)\n",
    "\n",
    "    # 画图 \n",
    "    fig_num = len(train_df.columns)  # 画两张图,分别是损失和准确率\n",
    "\n",
    "    # plt.subplots：用于创建一个包含多个子图的图形窗口。\n",
    "    # 1：表示子图的行数为 1。\n",
    "    # fig_num：表示子图的列数，即子图的数量。\n",
    "    # figsize=(5 * fig_num, 5)：设置整个图形窗口的大小，宽度为 5 * fig_num，高度为 5。\n",
    "    # fig：返回的图形对象（Figure），用于操作整个图形窗口。\n",
    "    # axs：返回的子图对象（Axes 或 Axes 数组），用于操作每个子图。\n",
    "    fig, axs = plt.subplots(1, fig_num, figsize=(5 * fig_num, 5))\n",
    "    for idx, item in enumerate(train_df.columns):\n",
    "        # train_df.index 是 x 轴数据（通常是 step）。\n",
    "        # train_df[item] 是 y 轴数据（当前指标的值）。\n",
    "        axs[idx].plot(train_df.index, train_df[item], label=\"train:\" + item)\n",
    "        # val_df.index 是 x 轴数据。\n",
    "        # val_df[item] 是 y 轴数据。\n",
    "        axs[idx].plot(val_df.index, val_df[item], label=\"val:\" + item)\n",
    "        axs[idx].grid()  # 显示网格\n",
    "        axs[idx].legend()  # 显示图例\n",
    "        # axs[idx].set_xticks(range(0, train_df.index[-1] + 1, 5000))  # 设置x轴刻度\n",
    "        # axs[idx].set_xticklabels(map(lambda x: f\"{x // 1000}k\", range(0, last_step + 1, 5000)))  # 设置x轴标签\n",
    "        axs[idx].set_xlabel(\"step\")\n",
    "\n",
    "    plt.show()\n",
    "\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": 20,
   "id": "eb47aada8173cbab",
   "metadata": {
    "ExecuteTime": {
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 10%|█         | 1960/19600 [01:22<12:26, 23.64it/s, epoch=9]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stop at epoch 10 / global_step 1960\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.5679, Test acc: 0.7184\n"
     ]
    }
   ],
   "source": [
    "epoch = 100\n",
    "\n",
    "# 单层单边\n",
    "model = RNN()\n",
    "model.to(device)\n",
    "\n",
    "# 1. 定义损失函数 采用二进制交叉熵损失, 先sigmoid再计算交叉熵\n",
    "loss_fct = F.binary_cross_entropy_with_logits\n",
    "# 等价于 loss_fct =nn.BCEWithLogitsLoss()\n",
    "\n",
    "# 2. 定义优化器\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
    "\n",
    "# 3.save model checkpoint\n",
    "if not os.path.exists(\"checkpoints\"):\n",
    "    os.makedirs(\"checkpoints\")\n",
    "save_ckpt_callback = SaveCheckpointsCallback(save_dir=\"checkpoints\", save_step=len(train_loader), save_best_only=True)\n",
    "\n",
    "# 4. early stopping\n",
    "early_stop_callback = EarlyStopCallback(patience=5, min_delta=0.01)\n",
    "\n",
    "# 训练过程\n",
    "record_dict = training(\n",
    "    model,\n",
    "    train_loader,\n",
    "    test_loader,\n",
    "    epoch,\n",
    "    loss_fct,\n",
    "    optimizer,\n",
    "    save_ckpt_callback=save_ckpt_callback,\n",
    "    early_stop_callback=early_stop_callback,\n",
    "    eval_step=len(train_loader)\n",
    ")\n",
    "\n",
    "plot_record_curves(record_dict) # 画图\n",
    "\n",
    "\n",
    "# 评估\n",
    "model.load_state_dict(\n",
    "    torch.load(\"checkpoints/02_embedding_rnn.ckpt\", weights_only=True, map_location=device))  # 加载最好的模型\n",
    "\n",
    "model.eval()  # 评估模式\n",
    "loss, acc = evaluate(model, test_loader, loss_fct)\n",
    "print(f\"Test loss: {loss:.4f}, Test acc: {acc:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f80f38b5b1cb76be",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-23T13:43:00.423803Z",
     "start_time": "2025-01-23T13:43:00.423803Z"
    },
    "execution": {
     "iopub.execute_input": "2025-01-23T14:05:20.096598Z",
     "iopub.status.busy": "2025-01-23T14:05:20.096069Z",
     "iopub.status.idle": "2025-01-23T14:06:40.871521Z",
     "shell.execute_reply": "2025-01-23T14:06:40.871010Z",
     "shell.execute_reply.started": "2025-01-23T14:05:20.096569Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  9%|▉         | 1764/19600 [01:16<12:54, 23.02it/s, epoch=8]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stop at epoch 9 / global_step 1764\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.6004, Test acc: 0.6812\n"
     ]
    }
   ],
   "source": [
    "epoch = 100\n",
    "\n",
    "# 单层双边\n",
    "model = RNN(bidirectional=True)\n",
    "model.to(device)\n",
    "\n",
    "# 1. 定义损失函数 采用二进制交叉熵损失, 先sigmoid再计算交叉熵\n",
    "loss_fct = F.binary_cross_entropy_with_logits\n",
    "# 等价于 loss_fct =nn.BCEWithLogitsLoss()\n",
    "\n",
    "# 2. 定义优化器\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
    "\n",
    "# 3.save model checkpoint\n",
    "if not os.path.exists(\"checkpoints\"):\n",
    "    os.makedirs(\"checkpoints\")\n",
    "save_ckpt_callback = SaveCheckpointsCallback(save_dir=\"checkpoints\", save_step=len(train_loader), save_best_only=True)\n",
    "\n",
    "# 4. early stopping\n",
    "early_stop_callback = EarlyStopCallback(patience=5, min_delta=0.01)\n",
    "\n",
    "# 训练过程\n",
    "record_dict = training(\n",
    "    model,\n",
    "    train_loader,\n",
    "    test_loader,\n",
    "    epoch,\n",
    "    loss_fct,\n",
    "    optimizer,\n",
    "    save_ckpt_callback=save_ckpt_callback,\n",
    "    early_stop_callback=early_stop_callback,\n",
    "    eval_step=len(train_loader)\n",
    ")\n",
    "\n",
    "plot_record_curves(record_dict) # 画图\n",
    "\n",
    "\n",
    "# 评估\n",
    "model.load_state_dict(\n",
    "    torch.load(\"checkpoints/02_embedding_rnn.ckpt\", weights_only=True, map_location=device))  # 加载最好的模型\n",
    "\n",
    "model.eval()  # 评估模式\n",
    "loss, acc = evaluate(model, test_loader, loss_fct)\n",
    "print(f\"Test loss: {loss:.4f}, Test acc: {acc:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4a4f776e5d5bfc29",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-23T14:06:40.874123Z",
     "iopub.status.busy": "2025-01-23T14:06:40.873863Z",
     "iopub.status.idle": "2025-01-23T14:07:53.746230Z",
     "shell.execute_reply": "2025-01-23T14:07:53.745679Z",
     "shell.execute_reply.started": "2025-01-23T14:06:40.874103Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  8%|▊         | 1568/19600 [01:08<13:12, 22.74it/s, epoch=7]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stop at epoch 8 / global_step 1568\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.5944, Test acc: 0.6900\n"
     ]
    }
   ],
   "source": [
    "epoch = 100\n",
    "\n",
    "# 双层单边\n",
    "model = RNN(num_layers=2)\n",
    "model.to(device)\n",
    "\n",
    "# 1. 定义损失函数 采用二进制交叉熵损失, 先sigmoid再计算交叉熵\n",
    "loss_fct = F.binary_cross_entropy_with_logits\n",
    "# 等价于 loss_fct =nn.BCEWithLogitsLoss()\n",
    "\n",
    "# 2. 定义优化器\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
    "\n",
    "# 3.save model checkpoint\n",
    "if not os.path.exists(\"checkpoints\"):\n",
    "    os.makedirs(\"checkpoints\")\n",
    "save_ckpt_callback = SaveCheckpointsCallback(save_dir=\"checkpoints\", save_step=len(train_loader), save_best_only=True)\n",
    "\n",
    "# 4. early stopping\n",
    "early_stop_callback = EarlyStopCallback(patience=5, min_delta=0.01)\n",
    "\n",
    "# 训练过程\n",
    "record_dict = training(\n",
    "    model,\n",
    "    train_loader,\n",
    "    test_loader,\n",
    "    epoch,\n",
    "    loss_fct,\n",
    "    optimizer,\n",
    "    save_ckpt_callback=save_ckpt_callback,\n",
    "    early_stop_callback=early_stop_callback,\n",
    "    eval_step=len(train_loader)\n",
    ")\n",
    "\n",
    "plot_record_curves(record_dict) # 画图\n",
    "\n",
    "\n",
    "# 评估\n",
    "model.load_state_dict(\n",
    "    torch.load(\"checkpoints/02_embedding_rnn.ckpt\", weights_only=True, map_location=device))  # 加载最好的模型\n",
    "\n",
    "model.eval()  # 评估模式\n",
    "loss, acc = evaluate(model, test_loader, loss_fct)\n",
    "print(f\"Test loss: {loss:.4f}, Test acc: {acc:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7629f1879f0b0c20",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-01-23T14:07:53.747125Z",
     "iopub.status.busy": "2025-01-23T14:07:53.746930Z",
     "iopub.status.idle": "2025-01-23T14:10:03.138228Z",
     "shell.execute_reply": "2025-01-23T14:10:03.137590Z",
     "shell.execute_reply.started": "2025-01-23T14:07:53.747104Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 14%|█▍        | 2744/19600 [02:05<12:49, 21.89it/s, epoch=13]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stop at epoch 14 / global_step 2744\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.5149, Test acc: 0.7630\n"
     ]
    }
   ],
   "source": [
    "epoch = 100\n",
    "\n",
    "# 双层双边\n",
    "model = RNN(num_layers=2, bidirectional=True)\n",
    "model.to(device)\n",
    "\n",
    "# 1. 定义损失函数 采用二进制交叉熵损失, 先sigmoid再计算交叉熵\n",
    "loss_fct = F.binary_cross_entropy_with_logits\n",
    "# 等价于 loss_fct =nn.BCEWithLogitsLoss()\n",
    "\n",
    "# 2. 定义优化器\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
    "\n",
    "# 3.save model checkpoint\n",
    "if not os.path.exists(\"checkpoints\"):\n",
    "    os.makedirs(\"checkpoints\")\n",
    "save_ckpt_callback = SaveCheckpointsCallback(save_dir=\"checkpoints\", save_step=len(train_loader), save_best_only=True)\n",
    "\n",
    "# 4. early stopping\n",
    "early_stop_callback = EarlyStopCallback(patience=5, min_delta=0.01)\n",
    "\n",
    "# 训练过程\n",
    "record_dict = training(\n",
    "    model,\n",
    "    train_loader,\n",
    "    test_loader,\n",
    "    epoch,\n",
    "    loss_fct,\n",
    "    optimizer,\n",
    "    save_ckpt_callback=save_ckpt_callback,\n",
    "    early_stop_callback=early_stop_callback,\n",
    "    eval_step=len(train_loader)\n",
    ")\n",
    "\n",
    "plot_record_curves(record_dict) # 画图\n",
    "\n",
    "\n",
    "# 评估\n",
    "model.load_state_dict(\n",
    "    torch.load(\"checkpoints/02_embedding_rnn.ckpt\", weights_only=True, map_location=device))  # 加载最好的模型\n",
    "\n",
    "model.eval()  # 评估模式\n",
    "loss, acc = evaluate(model, test_loader, loss_fct)\n",
    "print(f\"Test loss: {loss:.4f}, Test acc: {acc:.4f}\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
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